DSC 330 Casual Inference
An exploration of the methods and designs that allow researchers and analysts to examine causal relationships between treatments and responses, particularly when data are from observational studies. Topics include the potential outcomes framework, covariate balancing, propensity score methods, causal graphs, and regression discontinuity designs. The course emphasizes statistical computing in R as well as real-world data analysis and communication. Prerequisites: DSC 230 or DSC 205 or permission of the instructor.
Prerequisite
DSC 230, or permission from the instructor